Explainability in AI-based shift scheduling

Abstract

As digitalization becomes more widely used in factories, preference-based shift planning is evolving into an important tool for human-centered work in various workplaces. Human-centered shift planning not only increases the efficiency of work, but above all considers the individual's preferences for certain shifts or activities and thereby empowers human workers. However, the planning algorithm used in previous work is based on AI and the algorithm is not able to explain why certain decisions in scheduling were made. The aim of this publication is to use AI-based shift scheduling as an example to make AI systems comprehensible for everyone and thus increase the transparency of the system as well as the user’s trust. Starting from psychological research, we developed a user-friendly explanatory model, that consists of four parts: Starting section, what-if-scenarios, educational classroom and FAQs. A user survey was then conducted to test the effectiveness of the model. The results show that most respondents find the model intuitive and understandable, although they have different preferences for explanations. This study provides insights into the design of explanations for shift planning systems and examines the effects of different explanatory approaches. It thus is the foundation for further research and development in this area.

Keywords: Shift scheduling, Explainability, Transparency, Human System Interaction

DOI: 10.54941/ahfe1005831